Skip to main content
SpringerLink
Log in

Quantification of LOS at Uncontrolled Median Openings Using Area Occupancy Through Cluster Analysis

  • Research Article - Civil Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

This study quantifies LOS ranges for traffic movement at uncontrolled median openings using ‘area occupancy’ as a measure of effectiveness. The Highway Capacity Manual is silent about LOS ranges at uncontrolled median openings. Traditionally, traffic density is considered as an important parameter for quantifying the traffic flow. However, it does not consider the heterogeneous characteristics of the traffic stream. Further, occupancy also does not explain the heterogeneous traffic and the absence of lane discipline which is predominant in developing countries. Therefore, area occupancy is used in this study to assess the performance of traffic conditions at median openings. The established method to measure area occupancy has been modified to overcome the assumption used in earlier techniques. Area occupancy has been estimated for both major and minor traffic streams. K-mean clustering has been employed to classify area occupancy ranges for various LOS categories. This methodology could be beneficial for practitioner engineers to monitor the vehicular movement at median opening.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Patnaik, A.K.; Bhuyan, P.K.; Rao, K.K.: Divisive analysis (DIANA) of hierarchical clustering and GPS data for level of service criteria of urban streets. Alex. Eng. J. 55(1), 407–418 (2016)

    Article  Google Scholar 

  2. “Highway Capacity Manual”: Transportation Research Board. National Research Council, Washington, DC (2010)

  3. Sakai, T.; Yamada-Kawai, K.; Matsumoto, H.; Uchida, T.: New measure of the level of service for basic expressway segments incorporating customer satisfaction. Proc. Soc. Behav. Sci. 16, 57–68 (2011)

    Article  Google Scholar 

  4. Mohapatra, S.S.; Sil, G.; Dey, P.P.: Quantification of LOS at median openings through cluster analysis. Indian Highw. 43(3), 25–31 (2015)

    Google Scholar 

  5. Patnaik, A.K.; Krishna, Y.; Rao, S.; Bhuyan, P.K.: Development of roundabout entry capacity model using INAGA method for heterogeneous traffic flow conditions. Arab. J. Sci. Eng. 42(9), 4181–4199 (2017)

    Article  Google Scholar 

  6. Chandra, S.; Agrawal, A.; Rajamma, A.: Microscopic analysis of service delay at uncontrolled intersections in mixed traffic conditions. J. Transp. Eng. ASCE 135(6), 323–329 (2009)

    Article  Google Scholar 

  7. Mohanty, M.; Dey, P. P: Modelling the major stream delay due to U-turns. Transp. Lett. 1–8. https://doi.org/10.1080/19427867.2017.1401701 (2017)

  8. Ma, D.F.; Ma, X.L.; Jin, S.; Sun, F.; Wang, D.H.: Estimation of major stream delays with a limited priority merge. Canad. J. Civil Eng. 40(12), 1227–1233 (2013)

    Article  Google Scholar 

  9. Mohapatra, S.S.; Dey, P.P.: Lateral placement of U-turns at median openings on six-lane divided urban roads. Transp. Lett. 7(5), 252–263 (2015)

    Article  Google Scholar 

  10. Ashalatha, R.; Chandra, S.: Critical gap through clearing behavior of drivers at unsignalised intersections. KSCE J. Civil Eng. 15(8), 1427–1434 (2011)

    Article  Google Scholar 

  11. Washburn, S.S.; Kirschner, D.S: Rural freeway level of service based upon traveler perception. In: The 85th Annual Meeting of the Transportation Research Board, pp. 1–21 (2006)

  12. Choocharukul, K.; Sinha, K.C.; Mannering, F.L.: User perceptions and engineering definitions of highway level of service: an explanatory statistical comparison. Transp. Res. Part A 38, 677–689 (2004)

    Google Scholar 

  13. Kita, H.: Level-of-service measure of road traffic based on the driver’s perception. In: 4th International Symposium on Highway Capacity. Transportation Research Circular EC, vol. 18, pp. 53–62 (2000)

  14. Arasan, V.T.; Dhivya, G.: Measuring heterogeneous traffic density. In: Proceedings of International Conference on Sustainable Urban Transport and Environment, World Academy of Science, Engineering and technology, Bangkok, vol. 36, pp. 342–346 (2008)

  15. Mallikarjuna, C.; Rao, K.R.: Area occupancy characteristics of heterogeneous traffic. Transportmetrica 2(3), 223–236 (2006)

    Article  Google Scholar 

  16. Mathew, T.V.; C.R. Munigety; Bajpai, A.: Strip-based approach for the simulation of mixed traffic conditions. J. Comput. Civil Eng. ASCE, vol. 29(5), 04014069(1–9) (2013)

  17. Sil, G.; Mohapatra, S.S.; Dey, P.P.; Chandra, S: Merging process of U-turns at uncontrolled median openings under mixed traffic conditions, pp. 1–10. In: Transport. Taylor & Francis, Milton Park (2016)

  18. Mohapatra, S.S.; Dey, P.P.; Chandra, S.: Conflicting volume for U-turns at uncontrolled median openings. Proc. Inst. Civil Eng. Transp. 169(4), 195–204 (2016)

    Google Scholar 

  19. Jain, A.K.; Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall Inc, Upper Saddle River (1988)

    MATH  Google Scholar 

  20. Sarstedt, M.; Mooi, E.: A Concise Guide to Market Research. The Process Data. Springer, Berin (2014)

    Google Scholar 

  21. Kanungo, T.; Mount, D.M.; Netanyahu, N.S.; Piatko, C.D.; Silverman, R.; Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)

    Article  MATH  Google Scholar 

  22. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  MATH  Google Scholar 

  23. Arbelaitz, O.; Gurrutxaga, I.; Muguerza, J.; Pérez, J.M.; Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recognit. 46(1), 243–256 (2013)

    Article  Google Scholar 

  24. Pollard, K.S.; Van Der Laan, M.J.: A method to identify significant clusters in gene expression data. U.C. Berkeley Division of Biostatistics Working Paper Series, Working Paper 107 (2002)

  25. Boora, A.; Ghosh, I.; Chandra, S.: Clustering technique: an analytical tool in traffic engineering to evaluate the performance of two-lane highways. Transporti Europei 66, 1–18 (2017)

    Google Scholar 

  26. Jain, A.K.; Murty, M.N.; Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)

    Article  Google Scholar 

  27. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31(8), 651–666 (2010)

    Article  Google Scholar 

  28. Kanungo, T.; Mount, D.M.; Netanyahu, N.S.; Piatko, C.D.; Silverman, R.; Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)

    Article  MATH  Google Scholar 

  29. Berkhin, P.: A Survey of Clustering Data Mining Techniques Grouping Multidimensional Data, pp. 25–71. Springer, Berlin (2006)

    Book  Google Scholar 

  30. Yadav, J.; Sharma, M.: A review of K-mean algorithm. Int. J. Eng. Trends Technol. 4(7), 2972–75 (2013)

    Google Scholar 

  31. Biswas, S.; Chandra, S.; Ghosh, I.: Estimation of vehicular speed and passenger car equivalent under mixed traffic condition using artificial neural network. Arab. J. Sci. Eng. 42(9), 4099–4110 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

I hereby acknowledge my Ph.D. supervisor and co-author Dr. Partha Pratim Dey for always providing the proper direction to move forward in research. I also acknowledge Mr. Alok Kumar Samantaray, a fellowresearch scholar,who helped me to learn the basics ofMATLAB. Finally, I acknowledge my institute, IIT Bhubaneswar, for always helping me morally and financially to pursue my research.

Author information

Authors and Affiliations

  1. School of Infrastructure, Indian Institute of Technology, Bhubaneswar, Bhubaneswar, Odisha, India

    Malaya Mohanty & Partha Pratim Dey

Authors
  1. Malaya Mohanty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Partha Pratim Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Malaya Mohanty.

Ethics declarations

Funding Information

I am pursuing my Ph.D. at IIT Bhubaneswar, India, and get institute fellowship for my research from MHRD, India. Therefore, all my research funding is borne by MHRD, India, and IIT Bhubaneswar, India.

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohanty, M., Dey, P.P. Quantification of LOS at Uncontrolled Median Openings Using Area Occupancy Through Cluster Analysis. Arab J Sci Eng 44, 4667–4679 (2019). https://doi.org/10.1007/s13369-018-3509-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-018-3509-3

Keywords

Search

Navigation